Adaptive estimation of speech parameters

Linear predictive coding (LPC), and transformations of it, is currently the most popular way of analysing speech signals. Major limitations of using a frame-based technique are that each frame is analysed in isolation of the rest while assuming the excitation source to be a white noise process. In order to reduce computation time, an all pole model is usually employed. In the present project an adaptive algorithm is proposed for speech signal analysis. The algorithm is based on the recursive least squares method with a variable forgetting factor. A pole-zero model is used to estimate the anti-formants present in certain sounds (i.e. nasals and nasalized vowels). This method offers better detection of poles and zeros in stationary environments and faster tracking of pole and zero frequencies in nonstationary signals than other sequential methods. An effective input estimation algorithm eliminates the influence of pitch on the parameter estimates by assuming the input to be a white noise process or a pulse sequence.